Rainfall and corresponding Runoff estimation are substantially dependent on various geographic, climatic, and biotic features of the catchment or basin under study and these factors often induce a linear, non-linear or highly complex relation between rainfall and runoff. The few of key factors include precipitation, percolation, infiltration, evaporation, stream flow, and air temperature. Plenty of Rainfall-Runoff (RR) regression models are available, each one distinguished by a varying level of complexity and data requirement. Most of the time due to complex relationship between rainfall and runoff the traditional models (SCN-CN, MISDc, GA, CN4GA) with regression equations don't resembles the correct scene of rainfall-runoff connection. Computational Intelligence (CI) approaches play a key role in modeling those complex tie-ups between rainfall and runoff. The rainfall-runoff process was modeled using a mamdani Fuzzy Inference System (FIS) implemented within a layered design of Artificial Neural Network (ANN) and was applied to a small area of Koshi basin in Bihar, using 12 year's (1980–1992) observed records of daily rainfall, soil moisture and runoff. A comparison was also made between proposed models and existing soft computing models. The proposed computational intelligence model proves significantly better than existing soft computing models in terms of performance.